Joint Mode Selection and Power Control for Interference Management in D2D-Enabled Heterogeneous Cellular Networks

Device-to-device (D2D) communications have been recently proposed as an effective way to increase both spectrum and energy efficiency for future cellular systems. In this paper, joint mode selection, channel assignment, and power control in D2D communications are addressed. We aim at maximizing the overall system throughput while guaranteeing the signal-to-noise-and-interference ratio of both D2D and cellular links. Three communication modes are considered for D2D users: cellular mode, dedicated mode, and reuse mode. The optimization problem could be decomposed into two subproblems: power control and joint mode selection and channel assignment. The joint mode selection and channel assignment problem is NP-hard, whose optimal solution can be found by the branch-and-bound method, but is very complicated. Therefore, we develop low-complexity algorithms according to the network load. Through comparing different algorithms under different network loads, proximity gain, hop gain, and reuse gain could be demonstrated in D2D communications.

Joint mode selection and power control for D2D underlaid cellular networks

This paper explores the problem of joint mode selection, spectrum management, power control, and interference mitigation for device-to-device (D2D) communication underlaying a Long Term Evolution-Advanced (LTE-A) network. We consider a dynamic mode selection scenario, in which the modes (D2D or cellular) of the devices depend on optimal allocations. To improve the quality of service (QoS) for the users, the optimization objective in a corresponding problem is formulated in terms of buffer size of user equipments (UEs), which is estimated based on buffer status information collected by the UEs. The realizations of a resource allocation approach presented in the paper include its real-time and non-real-time implementations, as well as two modifications applicable to a standard LTE-Direct (LTE-D) network. Performance of the proposed algorithms has been evaluated using the OPNET-based simulations. All algorithms show improved performance in terms of mean packet end-to-end delay when compared to most relevant schemes proposed earlier.

We propose a distributed implementation of integrated sensing and communication (ISAC) underpinned by a massive multiple input multiple output (CF-mMIMO) architecture without cells. Distributed multi-antenna access points (APs) simultaneously serve communication users (UEs) and emit probing signals towards multiple specified zones for sensing. The APs can switch between communication and sensing modes, and adjust their transmit power based on the network settings and sensing and communication operations’ requirements. By considering local partial zero-forcing and maximum-ratio-transmit precoding at the APs for communication and sensing, respectively, we first derive closed-form expressions for the spectral efficiency (SE) of the UEs and the mainlobe-to-average-sidelobe ratio (MASR) of the sensing zones. Then, a joint operation mode selection and power control design problem is formulated to maximize the SE fairness among the UEs, while ensuring specific levels of MASR for sensing zones. The complicated mixed-integer problem is relaxed and solved via a successive convex approximation approach. We further propose a low-complexity design, where the AP mode selection is designed through a greedy algorithm and then power control is designed based on this chosen mode. Our findings reveal that the proposed scheme can consistently ensure a sensing success rate of 100% for different network setups with a satisfactory fairness among all UEs.

A joint power and rate control using game theory algorithm is proposed in heterogeneous networks environment with interference among different systems. It designs a novel revenue function based on the fairness of users' power control and the rate allocation, accompanying with the interference of the primary users caused by different users. The existence and uniqueness of the Nash equilibrium of game theory are derived by rigorous mathematical. Furthermore, simulation results show that the proposed algorithm can improve users' power control and rate allocation significantly.

In this paper, we develop an energy efficient resource allocation scheme for orthogonal frequency division multiple access (OFDMA) networks with in-band full-duplex (IBFD) communication between the base station and user equipments (UEs) considering a realistic self-interference (SI) model. Our primary aim is to maximize the system energy efficiency (EE) through a joint power control and sub-carrier assignment in both the downlink (DL) and uplink (UL), where the quality of service requirements of the UEs in DL and UL are guaranteed. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle this difficulty, we first use fractional programming to transform the fractional objective function to the subtractive form. Then, by employing Dinkelbach method, we propose an iterative algorithm in which an inner problem is solved in each iteration. Applying majorization-minimization approximation, we make the inner problem convex. Also, by introducing a penalty function to handle integer sub-carrier assignment variables, we propose an iterative algorithm for addressing the inner problem. We show that our proposed algorithm converges to the locally optimal solution which is also demonstrated by our simulation results. In addition, simulation results show that by applying the IBFD capability in OFDMA networks with efficient SI cancellation techniques, our proposed resource allocation algorithm attains a 75% increase in the EE as compared to the half-duplex system.

Joint power control and spectrum allocation for cognitive radio networks via pricing

In this paper, we present a game-theoretic approach for the purpose of deriving the problem of joint beamforming and power control in cognitive radio (CR) multiple-input multiple-output (MIMO) broadcast channels (CR MIMO-BCs), where the primary users (PUs) coexist with the secondary users (SUs) and they share the same spectrum. The cognitive base station (CBS), which is equipped with multiple antennas, is capable of transmitting data to the SU’s multiple-antenna receiver by employing the technology of beamforming. The proposed approach is an application of separable games, which are formally stated by the subgames of beamforming and power control. Furthermore, based on the model of noncooperative separate games, separable cost functions for the parameters of beamforming and power control are also proposed, showing that these cost functions are convex. Therefore, the convex theory of a noncooperative game can be employed to investigate the best response strategies as well as existence of Nash equilibrium solutions. Finally, we propose an iterative algorithm to achieve the optimal Nash equilibrium of the proposed joint beamforming subgame and power control subgame. Numerical results verify both the convergence and the tracking properties of the proposed algorithm for variant scenarios.

Transmitter adaptation in conjunction with power control enables individual users in multi-user wireless systems to meet specific quality of service (QoS) constraints imposed in terms of target values for the users' signal-to-interference+noise-ratio (SINR) at their intended receiver. Existing algorithms for joint transmitter adaptation and power control require knowledge of channel state information (CSI) which is in general difficult to acquire in wireless environments that are subject to fading. In this paper we study the performance of incremental algorithms for joint transmitter adaptation and power control with fading channels by formally defining a probability of outage measure and evaluating it through Monte-Carlo simulations in specific scenarios.

Device-to-device (D2D) multicast transmission is an important feature for group-oriented applications. In this paper, we propose a joint channel allocation and power control scheme for the D2D multicast underlay communications. In single-rate multicast, the achieved data rate is determined by the weakest link. Therefore, we formulate this MINLP problem as a maximin optimization problem to guarantee fairness of different multicast groups. Each D2D group can reuse the channel of all cellular users. In order to limit the impact of D2D communications on cellular users' quality of service (QoS), we define an SINR threshold value for the cellular users. The non-linear Perron-Frobenius theory is used to derive a deterministic algorithm for solving the non-convex non-linear fair power control problem. A novel-genetic-algorithm-aided efficient scheme is then proposed to solve the combinatory issue of the joint channel allocation and power control. The proposed scheme is evaluated by using extensive simulations. Numerical results highlight the performance of our approach in terms of sum rate fairness.

In this paper, we study joint link scheduling and power control with successive interference cancellation (SIC), aiming at maximizing the α-utility. The joint link scheduling and power control with SIC (PCSIC) problem is formulated to be a mixed-integer non-linear programming (MINLP), which is NP-hard. In order to solve the problem, we first decompose the MINLP into three sub-problem and then propose an iterative algorithm. We compare our strategy with the scheme without power control from the perspective of system throughput, fairness index and energy consumption. Numerical results show the noticeable performance improvement of the proposed strategy.

It is significantly important to reduce uplink energy consumption in order to prolong the battery lifetime of user equipment (UE). In this paper, an uplink energy-saving power control scheme is proposed based on Coordinated Multiple Points Reception (CoMP) in the heterogeneous cellular networks. The main idea of the proposed scheme is based on the tradeoff between the uplink transmission power and system throughput. Firstly, cooperative UE obtains the gain of throughput via CoMP. Then, an energy-saving power control strategy is adopted to reduce transmission power of CoMP UE on the premise that transmission throughput is guaranteed. Simulation results show that the proposed scheme can effectively reduce the UE's transmission power and obtain approximately 9% gain in energy efficiency (EE) compared to conventional non-cooperation uplink transmission scheme.

Device-to-Device (D2D) communication underlaying cellular networks is expected to bring significant benefits for resource utilization and cellular coverage. However, the resource allocation and power control needs elaborate coordination, otherwise it may cause severe interference between D2D user equipment (DUE) and cellular user equipment (CUE). In this paper, we study the joint radio resource allocation and power control problem with energy efficiency as our optimization goal. To improve the system energy efficiency with low computational complexity, we propose an iterative combinatorial auction algorithm with flexible power control method, where the CUEs are considered as bidders, DUEs as goods and the cellular network plays a role as the auctioneer controlling the auction process. We also analyze the properties of the proposed algorithm and present numerical results to verify that our algorithm can significantly improve energy efficiency.

This paper considers wireless uplink information and downlink power transfer in cell-free massive multiple-input multiple-output systems. The single-antenna user equipments (UEs) utilize the energy harvested in the downlink to transmit uplink pilot and information signals to the multiple-antenna access points (APs). We consider Rician fading and maximum ratio processing based on either linear minimum mean-squared error (LMMSE) or least-squares (LS) channel estimation. We derive the average harvested energy by using a practical non-linear energy harvesting circuit model for both coherent and non-coherent transmission schemes. Furthermore, the uplink spectral efficiency (SE) is derived for all the considered methods and the max-min fairness problem is cast where the optimization variables are the AP and UE power control coefficients together with the large-scale fading decoding vectors. The objective is to maximize the minimum SE of the UEs' under APs' and UEs' transmission power constraints. A novel alternating optimization algorithm with guaranteed convergence and improvement at each step is proposed to solve the highly-coupled non-convex problem.

In this paper, we investigate a joint scheduling and power control for a multi-cell cognitive radio network (CRN) in coexistence with a cellular primary radio network (PRN). The PRN uses a set of licensed orthogonal channels for transmission. This set of channels is also accessed in an opportunistic manner by a set of cognitive radio base stations (CR-BSs) to support secondary users (SUs). The problem is formulated to maximize the spectrum utilization of SUs without causing excessive interference to active primary users (PUs) of the PRN. In addition, all the served SUs must meet a certain QoS. A centralized solution for joint scheduling and power control is derived to give the global accessing decision for all unserved SUs. With the assumption that the knowledge of all subscribers is available, the coordinator of the CRN can use the joint scheduling and power control algorithm to maximize the spectrum utilization of served SUs by solving a mixed-integer linear programming (MILP) with an NP-hard complexity. To avoid the NP-hard complexity, we propose a sub-optimal heuristic algorithm that can be obtained at a much lower complexity based on the coloring interference graph among unserved SUs effected by served SUs and active PUs. Simulation results show that the proposed algorithm can yield the performance close to the optimal solution's.